Overall Corridor Analysis

Overview

# ZOE - what's going on here? something we can delete? 

The Olney transit corridor runs along Chew Av , and Olney Av between Locust Av and Front St. The corridor serves the routes 18, and 26. There are 21 total bus stops along the corridor, with the average bus trip running past 10 stops spanning 1.08 miles.

# map of routes that exist in this corridor
leaflet() %>%
  setView(lng = -75.14511, lat = 40.03905, zoom = 13) %>% 
  addProviderTiles(providers$Stamen.Toner) %>% 
  addCircleMarkers(daily_stop_analytics, lat = daily_stop_analytics$stop_lat, lng = daily_stop_analytics$stop_lon, radius = (daily_stop_analytics$total_ons + daily_stop_analytics$total_offs)/100, color = "blue")
  #addPolylines(data = routes_w_ridership, color = "#4377bc", weight = 4, layerId = link_stop_data$fromto, opacity = 0.5)

# also add chart/table of global averages for context of subcorridors

Full Corridor Analytics

These charts illustrate characteristics for the Olney corridor as a whole, such as average speed and ridership. Average speed, as well as ridership., vary both by route and by time of day.

Full Corridor Daily Analytics

table_1 <- full_corridor_results$route_analytics[[1]] %>% bind_rows(full_corridor_results$analytics[[1]] %>% mutate(route_id = "Total"))
 
kable(table_1, booktabs = TRUE, align = 'c',format.args = list(big.mark = ","),digits=1) %>%
  kable_styling(latex_options = "scale_down")  %>%
  row_spec(dim(table_1)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) %>%  # format first column
  scroll_box(width = "100%", height = "300px")
route_id daily_ridership trips routes_served service_hours riders_per_hour on_off dwell_observed_mean dwell_predicted_mean dwell_hybrid_mean dwell_per_onoff onoff_per_trip onoff_per_tripstop avg_segment_speed avg_speed_10_pct avg_speed_25_pct avg_speed_75_pct avg_speed_90_pct
18 6,826 235 18 19.8 345.1 3,896.5 0 83.7 83.7 0.2 16.6 1.5 12.0 9.6 10.3 12.6 14.5
26 4,966 227 26 20.1 247.4 2,072.4 0 71.0 71.0 0.1 9.1 0.9 11.1 9.6 10.3 11.9 13.2
Total 11,792 462 18, 26 39.9 295.9 5,968.9 0 77.5 77.5 0.2 12.9 1.2 11.6 9.6 10.3 12.1 14.0

Weekday Ridership

Speed and Reliability

Speed and reliability are two of the most important aspects governing how attractive transit is to the customer.

There is, however, an important distinction between transit speed and transit reliability: - speed is how fast the vehicle is moving through the corridor - reliability is how consistent those speeds are, throughout a day or another period of time.

Oftentimes, qualitative research on transit finds that riders remember their worst trip much more vividly than their average (or even their best) trips. This is where reliability is key - providing customers with a consistent trip time is just as important as a fast trip time, because when they budget time for future trips, they have to be reasonably sure that the time budgeted will represent the majority of potential travel time outcomes.

Speed
Reliability

End-to-End Travel Time

Corridor-level travel time is simply the amount of time it takes the bus to travel from one end of the corridor to another. In this case, it is show separated by route and by direction and is averaged for each hour.

Service Hours

Service hours are a measure of how much transit is operated on the corridor. It is simply the sum of all of the runtime on the corridor over a period of time. Service hours is a product of how much transit is provide, but it is also a product of the speed of operations on a corridor. Transit productivity is often measured in terms of service hours because it is the most direct input into the cost of running the service.

Sub-Corridor Analysis

Olney and Chelten - Olney and Broad Analytics

Interpretation

Subcorridor Daily Analytics

route_id daily_ridership trips routes_served service_hours riders_per_hour on_off dwell_observed_mean dwell_predicted_mean dwell_hybrid_mean dwell_per_onoff onoff_per_trip onoff_per_tripstop avg_segment_speed avg_speed_10_pct avg_speed_25_pct avg_speed_75_pct avg_speed_90_pct
18 6,826 235 18 19.8 345.1 3,896.5 0 83.7 83.7 0.2 16.6 1.5 12.0 9.6 10.3 12.6 14.5
26 4,966 227 26 20.1 247.4 2,072.4 0 71.0 71.0 0.1 9.1 0.9 11.1 9.6 10.3 11.9 13.2
Total 11,792 462 18, 26 39.9 295.9 5,968.9 0 77.5 77.5 0.2 12.9 1.2 11.6 9.6 10.3 12.1 14.0

Weekday Ridership

Speed and Reliability

Speed
Reliability

End-to-End Travel Time

Olney and Broad - Olney and 7th Analytics

Interpretation

Subcorridor Daily Analytics

route_id daily_ridership trips routes_served service_hours riders_per_hour on_off dwell_observed_mean dwell_predicted_mean dwell_hybrid_mean dwell_per_onoff onoff_per_trip onoff_per_tripstop avg_segment_speed avg_speed_10_pct avg_speed_25_pct avg_speed_75_pct avg_speed_90_pct
18 8,505 235 18 11.6 731.8 5,515.5 0 82.8 82.8 0.3 23.5 3.9 10.3 8.0 8.7 11.3 12.9
26 6,992 229 26 15.1 462.2 5,542.4 0 75.2 75.2 0.3 24.2 3.5 11.5 9.3 10.2 12.5 14.2
Total 15,497 464 18, 26 26.8 579.3 11,057.9 0 79.0 79.0 0.3 23.8 3.4 10.9 8.4 9.3 12.1 13.8

Weekday Ridership

Speed and Reliability

Speed
Reliability

End-to-End Travel Time

Olney and 7th - Olney and Front Analytics

Interpretation

Subcorridor Daily Analytics

route_id daily_ridership trips routes_served service_hours riders_per_hour on_off dwell_observed_mean dwell_predicted_mean dwell_hybrid_mean dwell_per_onoff onoff_per_trip onoff_per_tripstop avg_segment_speed avg_speed_10_pct avg_speed_25_pct avg_speed_75_pct avg_speed_90_pct
18 5,981 223 18 -10.5 -568.3 2,036.3 0 52.2 52.2 0.2 9.1 1.5 10.5 8.2 8.9 11.4 14.1
26 4,896 229 26 14.2 345.8 2,224.6 0 53.7 53.7 0.2 9.7 1.4 10.0 8.2 8.9 10.8 12.5
Total 10,877 452 18, 26 3.6 2,994.6 4,261.0 0 53.0 53.0 0.2 9.4 1.4 10.3 8.2 8.9 11.0 13.3

Weekday Ridership

Speed and Reliability

Speed
Reliability

End-to-End Travel Time